[Digital transformation of COVID-19 research]

In a current life sciences research, we are in an era in which advanced technology emerging and utilize big data. Data-driven approaches such as machine learnings play an important role to analyze these datasets. However, limited clinical (time-course) datasets are available for infectious diseases, cancer, and other diseases. Especially in the case of emerging infectious disease outbreaks, clinical data obtained from a limited number of cases must be used to develop treatment strategies and public health policies. This means that many clinical data are not big data, which often makes the application of data-driven approaches difficult. In this paper, we mainly apply a mathematical model-based approach to the clinical data of COVID-19 and discuss how biologically important information can be extracted from the limited data and how they can benefit society.